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Momentum-based distributed gradient tracking algorithms for distributed aggregative optimization over unbalanced directed graphs
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DOI：10.1016/j.automatica.2024.111596.png)
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This paper studies a distributed aggregative optimization problem over a directed graph with the rowstochastic weighted matrix. Different from the existing work on distributed optimization, the local cost function of each agent depends both on its local decision variable and on the sum of all functions formed by the decision variables of all agents. Inspired by the distributed dynamic average consensus protocol, heavy-ball strategy, and Nesterov gradient descent method, a momentum-based distributed gradient tracking algorithm with a fixed step size is proposed to solve such a problem. Further, it is shown that the proposed algorithm has a linear convergence rate if the global cost function is strongly convex with the Lipschitz-continuous gradient. The upper bounds of the fixed step size and the momentum parameter are restricted by a sufficiently small positive constant, respectively. Finally, a numerical example is provided to verify the effectiveness of the findings. (c) 2024 Elsevier Ltd. All rights reserved.
Keyword:
Distributed aggregative optimization
Row -stochastic weighted matrix
Gradient tracking
Acceleration
Linear convergence
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
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Surplus-based accelerated algorithms for distributed optimization over directed networks.png)
AUTOMATICA
AUTOMATICAIF5.9
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